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As an emerging technology to support scalable content-based image retrieval (CBIR), hashing has been recently received great attention and became a very active research domain. In this study, we propose a novel unsupervised visual hashing approach called semantic-assisted visual hashing (SAVH). Distinguished from semi-supervised and supervised visual hashing, its core idea is to effectively extract the rich semantics latently embedded in auxiliary texts of images to boost the effectiveness of visual hashing without any explicit semantic labels. To achieve the target, a unified unsupervised framework is developed to learn hash codes by simultaneously preserving visual similarities of images, integrating the semantic assistance from auxiliary texts on modeling high-order relationships of inter-images and characterizing the correlations between images and shared topics. Our performance study on three publicly available image collections:Wiki, MIR Flickr, and NUS-WIDEindicates that SAVH can achieve superior performance over several state-of-the-art techniques.

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